标题： NAEx：一种用于解释网络对齐的即插即用框架 显示英文标题

标题： NAEx: A Plug-and-Play Framework for Explaining Network Alignment

摘要： 网络对齐（NA）识别多个网络中的对应节点，在社交网络、合著关系和生物学等领域有应用。 尽管对齐模型有了进展，但其可解释性仍然有限，使得理解对齐决策变得困难，并在高风险领域中建立信任带来了挑战。 为了解决这个问题，我们引入了NAEx，这是一个即插即用、与模型无关的框架，通过识别影响预测的关键子图和特征来解释对齐模型。 NAEx通过以下方式解决了在对齐决策中保持联合跨网络依赖性的关键挑战：（1）通过可学习的边和特征掩码联合参数化图结构和特征空间，（2）引入一个优化目标，确保解释既忠实于原始预测，又能实现网络之间结构和特征相似性的有意义比较。 NAEx是一个归纳框架，能够高效地为之前未见过的数据生成NA解释。 我们引入了针对对齐可解释性的评估指标，并通过将其与四个代表性的NA模型结合，在基准数据集上展示了NAEx的有效性和效率。 显示英文摘要

摘要： Network alignment (NA) identifies corresponding nodes across multiple networks, with applications in domains like social networks, co-authorship, and biology. Despite advances in alignment models, their interpretability remains limited, making it difficult to understand alignment decisions and posing challenges in building trust, particularly in high-stakes domains. To address this, we introduce NAEx, a plug-and-play, model-agnostic framework that explains alignment models by identifying key subgraphs and features influencing predictions. NAEx addresses the key challenge of preserving the joint cross-network dependencies on alignment decisions by: (1) jointly parameterizing graph structures and feature spaces through learnable edge and feature masks, and (2) introducing an optimization objective that ensures explanations are both faithful to the original predictions and enable meaningful comparisons of structural and feature-based similarities between networks. NAEx is an inductive framework that efficiently generates NA explanations for previously unseen data. We introduce evaluation metrics tailored to alignment explainability and demonstrate NAEx's effectiveness and efficiency on benchmark datasets by integrating it with four representative NA models.